I-Corps: Contextualization of Explainable Artificial Intelligence (AI) for Better Health

I-Corps：可解释人工智能 (AI) 的情境化以改善健康

• 批准号: 2331366
• 负责人: Ying Ding
• 金额: $ 5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331366&HistoricalAwards=false
• 关键词: Corps; Contextualization; Explainable; Artificial; Intelligence

The broader impact/commercial potential of this I-Corps project is the development of the explainable Artificial Intelligence (XAI) methods for healthcare data. Currently, the number of electronic medical records is increasing while machine learning and deep learning models, especially large language models, have been employed to address healthcare needs. However, the healthcare domain is highly regulated and explainability for the black-box AI model becomes increasingly critical for any AI application. Users need to comprehend and trust the results and output created by machine learning algorithms. The proposed XAI technology may be used to describe an AI model, its expected impact, and potential biases. Further, the proposed technology may be used to transfer AI predictions into explainable medical interventions to enable the last mile delivery of AI in healthcare The commercial potential of these technologies may impact three major groups: health insurance companies who may provide better care management interventions and achieve personalized care delivery based on XAI; health analytic companies who rely on explanation to further enhance their products and meet the government regulations; and medical device startups who demand explainable analytical outputs based on the collected data from medical devices to enrich their user experience.This I-Corps project is based on the development of explainable Artificial Intelligence (XAI) methods applied to the healthcare industry. Providing explainability is critical for AI health applications. Healthcare is a unique domain with multimodality data: tableau data about patient demographic information, textual data about medical notes, time series data about vital sign measures, images about medical scan, and wavelet data about EEG and ECG. To provide a holistic view of these data, deep learning is used to create universal embeddings on different modalities of data and build the prediction models for health risks. But deep learning methods lack transparency and demand explainability. The proposed technology combines integrated gradients with ablation studies to identify the contributing factors of different data components in the explanation. In addition, the proposed platform adds knowledge graphs into the prediction and explanation workflow to detect the relationships between contributing features to generate an explanation with a holistic view, and translates weights or feature importance into risk scores to enable the last mile delivery of AI in healthcare. The proposed XAI method may be used to explain the importance of input data components, identify the contributing features at the individual patient level and the patient cohort level; scale and save computational resources; and self-improve by using reinforcement learning to enhance positive feedback.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个I-Corps项目的更广泛的影响/商业潜力是为医疗数据开发可解释的人工智能（XAI）方法。 目前，电子医疗记录的数量正在增加，而机器学习和深度学习模型，特别是大型语言模型，已被用于解决医疗保健需求。 然而，医疗保健领域受到高度监管，黑箱AI模型的可解释性对于任何AI应用程序都变得越来越重要。 用户需要理解和信任机器学习算法创建的结果和输出。 拟议的XAI技术可用于描述AI模型、其预期影响和潜在偏差。 这些技术的商业潜力可能会影响三个主要群体：健康保险公司，他们可以提供更好的护理管理干预措施，并基于XAI实现个性化护理服务;健康分析公司，他们依靠解释来进一步提高他们的产品并满足政府法规;以及医疗设备初创公司，他们需要基于从医疗设备收集的数据进行可解释的分析输出，以丰富其用户这个I-Corps项目是基于应用于医疗保健行业的可解释人工智能（XAI）方法的开发。 提供可解释性对于AI健康应用至关重要。医疗保健是一个具有多模态数据的独特领域：有关患者人口统计信息的表格数据、有关医疗笔记的文本数据、有关生命体征测量的时间序列数据、有关医学扫描的图像以及有关EEG和ECG的小波数据。为了提供这些数据的整体视图，深度学习用于在不同形式的数据上创建通用嵌入，并构建健康风险的预测模型。但深度学习方法缺乏透明度，需要可解释性。 所提出的技术将综合梯度与消融研究相结合，以识别解释中不同数据成分的影响因素。 此外，该平台还将知识图添加到预测和解释工作流程中，以检测贡献特征之间的关系，从而生成具有整体视图的解释，并将权重或特征重要性转换为风险评分，以实现医疗保健中AI的最后一英里交付。 所提出的XAI方法可用于解释输入数据成分的重要性，识别个体患者水平和患者群组水平的贡献特征;缩放和节省计算资源;和自该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。